Machine Learning QA Engineer

Machine Learning QA Engineer (Remote - Worldwide)

Derq, an MIT spinoff, is at the forefront of transforming urban mobility through AI-powered traffic safety and smart infrastructure. We are a passionate team of innovators leveraging cutting-edge AI and technology to create safer and smarter cities. Our patented technology analyzes real-time data from connected sensors to predict and prevent road incidents, deploying both edge and cloud solutions.

About the Role

As a Machine Learning Quality Assurance Engineer at Derq, you will be instrumental in guaranteeing the reliability, accuracy, and performance of our computer vision and machine learning-based products. You will collaborate closely with our development team to craft and implement robust testing strategies, identify and report defects, and contribute to enhancing the overall quality of our customer-facing AI products through data analytics.

Key Responsibilities

• Collaborate with AI development and product teams to understand requirements and design effective test plans and cases for AI-based products.
• Strategize evaluation methodologies, gather data, and prepare evaluation reports.
• Perform data analytics on large datasets.
• Monitor and track anomalies within product content and statistics.
• Train and deploy ML models to enhance product performance.
• Create and maintain detailed documentation of test processes, methodologies, and findings.
• Stay updated with advancements in the field to optimize internal processes and workflows.
• Interface and coordinate with the Engineering team.
• Foster a collaborative and proactive team environment that values shared success.

Requirements

• Bachelor’s degree in an analytical field such as Machine Learning, Computer Science, or a related discipline.
• Proficiency in Python, Javascript, and SQL.
• Familiarity with classical Machine Learning models (e.g., Random Forest, SVM, Naive Bayes).
• Core understanding of mathematical concepts for statistical inference.
• Experience with spreadsheet-based querying and analytics.
• Proficiency with annotation tools such as CVAT.
• Excellent knowledge of word processing tools and spreadsheets (e.g., MS Office Word, Excel).
• Good command of English, both oral and written, with strong reporting skills.
• Knowledge of basic statistical principles, coupled with logical thinking, coherent reasoning, and a detail-oriented attitude.

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